Open AccessMultimodal integration of EEG and MEG data: A simulation study with variable signal‐to‐noise ratio and number of sensors
Author(s) -
Babiloni Fabio,
Babiloni Claudio,
Carducci Filippo,
Romani Gian Luca,
Rossini Paolo M.,
Angelone Leonardo M.,
Cincotti Febo
Publication year - 2004
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20011
Subject(s) - electroencephalography , magnetoencephalography , signal to noise ratio (imaging) , noise (video) , computer science , pattern recognition (psychology) , inverse , ranging , mathematics , statistics , speech recognition , artificial intelligence , psychology , neuroscience , telecommunications , geometry , image (mathematics)
Abstract Previous simulation studies have stressed the importance of the multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data in the estimation of cortical current density. In such studies, no systematic variations of the signal‐to‐noise ratio (SNR) and of the number of sensors were explicitly taken into account in the estimation process. We investigated effects of variable SNR and number of sensors on the accuracy of current density estimate by using multimodal EEG and MEG data. This was done by using as the dependent variable both the correlation coefficient (CC) and the relative error (RE) between imposed and estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were: (1) the SNR of the simulated scalp data (with seven levels: infinite, 30, 20, 10, 5, 3, 1); (2) the particular inverse operator used to estimate the cortical source activity from the simulated scalp data (INVERSE, with two levels, including minimum norm and weighted minimum norm); and (3) the number of EEG or MEG sensors employed in the analysis (SENSORS, with three levels: 128, 61, 29 for EEG and 153, 61, or 38 in MEG). Analysis of variance demonstrated that all the considered factors significantly affect the CC and the RE indexes. Combined EEG–MEG data produced statistically significant lower RE and higher CC in source current density reconstructions compared to that estimated by the EEG and MEG data considered separately. These observations hold for the range of SNR values presented by the analyzed data. The superiority of current density estimation by multimodal integration of EEG and MEG was not due to differences in number of sensors between unimodal (EEG, MEG) and combined (EEG–MEG) inverse estimates. In fact, the current density estimate relative to the EEG–MEG multimodal integration involved 61 EEG plus 63 MEG sensors, whereas estimations carried out with the single modalities alone involved 128 sensors for EEG and 153 sensors for MEG. The results of the simulations also suggest that the use of simultaneous 29 EEG sensors during the MEG measurements carried out with full sensor arrangements (153 sensors) returned an accuracy of the cortical source estimate statistically similar to that obtained by combining 64 EEG and 153 MEG sensors. Hum. Brain Mapp. 22:54–64, 2004. © 2004 Wiley‐Liss, Inc.